This is a preview of Chapter 1 from my new ebook – Strategic SEO 2025 – a PDF which is available to download for free here.
The antitrust case, United States et al. v. Google LLC, initiated by the US Department of Justice (DOJ) in 2020, represents the most significant legal challenge to Google’s market power in a generation.
While the legal arguments focused on market monopolisation, the proceedings inadvertently became a crucible for technical disclosure, forcing Google to all but reveal the long-guarded secrets of its search engine architecture.
The trial’s technical revelations were not incidental; they were central to the core legal conflict.
The DOJ’s case rested on the premise that Google unlawfully maintained its monopoly in general search and search advertising through a web of anticompetitive and exclusionary agreements with device manufacturers and browser developers, including Apple, Samsung, and Mozilla.
These contracts, often involving payments of billions of dollars annually, ensured Google was the pre-set, default search engine for the vast majority of users, thereby foreclosing competition by denying rivals the scale and data necessary to build a viable alternative.
This legal challenge created a strategic paradox for Google.
To counter the DOJ’s accusation that its dominance was the result of illegal exclusionary contracts, Google’s primary defence was to argue that its success is a product of superior quality and continuous innovation – that users and partners choose Google because it is simply the best search engine available.
This “superior product” defence, however, could not be asserted in a vacuum.
To substantiate the claim, Google was compelled to present evidence of this superiority, which necessitated putting its top engineers and executives on the witness stand. Individuals like Pandu Nayak, Google’s Vice President of Search, and Elizabeth Reid, Google’s Head of Search, were tasked with explaining, under oath, the very systems that produce this acclaimed quality.
Consequently, the act of defending its market position legally forced Google to compromise its most valuable intellectual property and its long-held strategic secrecy.
The sworn testimonies and internal documents entered as evidence provided an unprecedented, canonical blueprint of Google’s key competitive advantages.
At the heart of these revelations is the central role of user interaction data.
A recurring theme throughout the testimony was that Google’s “magic” is not merely a static algorithm but a dynamic, learning system engaged in a “two-way dialogue” with its users.
Every click, every scroll, and every subsequent query is a signal that might teach the system what users find valuable.
This continuous feedback loop, operating at a scale that Google’s monopoly ensures no competitor can replicate, is the foundational resource for the powerful ranking systems detailed in the trial.
The Architecture of Google Search Ranking
The trial testimony and exhibits dismantle the popular conception of Google’s ranking system as a single, monolithic algorithm.
Instead, they reveal a sophisticated, multi-stage pipeline composed of distinct, modular systems, each with a specific function and data source.
This architecture is built upon a foundation of traditional information retrieval principles and human-engineered logic, which is then powerfully refined by systems that leverage user behaviour data at an immense scale.
The following analysis details the core components of this architecture, introducing a new lexicon – Topicality (T*), Navboost, and Q* – that is essential for understanding the modern Google search engine.
| System Name | Primary Function | Key Data Inputs | Engineering Approach | Key Revelation Source |
| Topicality (T*) | Determines a document’s direct relevance to query terms. | Anchors (A), Body (B), Clicks (C). | Hand-crafted by engineers. | HJ Kim Deposition |
| Navboost | Refines rankings based on historical user satisfaction. | 13 months of aggregated user click data (good/bad/lastLongest clicks). | Data-driven system, refined by engineers. | Pandu Nayak Testimony |
| Quality Score (Q*) | Assesses the overall trustworthiness and quality of a website/domain. | PageRank, link distance from trusted “seed” sites. | Hand-crafted, largely static score. | Trial Exhibits, Engineer Deposition |
| RankBrain | Interprets novel, ambiguous, and long-tail search queries. | Historical search data (not live user data). | Machine Learning (unsupervised). | Eric Lehman/Pandu Nayak Testimony |
Information Retrieval and the Primacy of “Hand-Crafted” Signals
Contrary to the prevailing narrative of an all-encompassing artificial intelligence, the trial revealed that Google’s search ranking systems are fundamentally grounded in signals that are “hand-crafted” by its engineers.
This deliberate engineering philosophy prioritises control, transparency, and the ability to diagnose and fix problems, a stark contrast to the opaque, “black box” nature of more complex, end-to-end machine learning models.
The deposition of Google Engineer HJ Kim was particularly illuminating on this point. He testified that “the vast majority of signals are hand-crafted,” explaining that the primary reason for this approach is so that “if anything breaks, Google knows what to fix“.
This methodology is seen as a significant competitive advantage over rivals like Microsoft’s Bing, which was described as using more complex and harder-to-debug ML techniques.
The process of “hand-crafting” involves engineers analysing relevant data, such as webpage content, user clicks, and feedback from human quality raters, and then applying mathematical functions, like regressions, to define the “curves” and “thresholds” that determine how a signal should respond to different inputs.
This human-in-the-loop system ensures that engineers can modify a signal’s behaviour to handle edge cases or respond to public challenges, such as the spread of misinformation on a sensitive topic.
This foundational layer of human-engineered logic provides the stability and predictability upon which more dynamic systems are built.
Trustworthiness
“Q* (page quality (i.e., the notion of trustworthiness)) is incredibly important. If competitors see the logs, then they have a notion of “authority” for a given site.” February 18, 2025, Call with Google Engineer HJ Kim (DOJ Case)
I agree – if this information were made available, it would be abused.
The emergence of these distinct systems – T* for query-specific relevance, Q* for static site quality, and Navboost for dynamic user-behaviour refinement – paints a clear picture of a modular, multi-stage ranking pipeline.
The process does not rely on a single, all-powerful algorithm.
Instead, it appears to be a logical sequence: initial document retrieval is followed by foundational scoring based on relevance (T*) and trust (Q*).
This scored list is then subjected to a massive re-ranking and filtering process by Navboost, which leverages the collective historical behaviour of users.
Only the small, refined set of results that survives this process is passed to the final, most computationally intensive machine learning models.
This architecture elegantly balances the need for speed, scale, and accuracy, using less expensive systems to do the initial heavy lifting before applying the most powerful models.
Freshness (Timeliness of Content)
Google also considers freshness – how recent or up-to-date the information on a page is, especially for queries where timeliness matters.
Trial testimony and exhibits detailed how freshness influences rankings:
- Freshness as a Relevance Signal: “Freshness is another signal that is ‘important as a notion of relevance’,” Pandu Nayak testified regmedia.co.uk. In queries seeking current information, newer content can be more relevant. Nayak gave an example: if you’re searching for the latest sports scores or today’s news, “you want the pages that were published maybe this morning or yesterday, not the ones that were published a year ago.” regmedia.co.uk Even if an older page might have been relevant in general, it won’t satisfy a user looking for the latest updates. Thus, Google’s ranking system will favour more recently published pages for fresh information queries. Conversely, for topics where age isn’t detrimental (say, a timeless recipe or a classic novel), an older authoritative page can still rank well. As Nayak put it, “deciding whether to use [freshness] or not is a crucial element” of delivering quality results regmedia.co.uk – Google must judge when recency should boost a result’s ranking and when it’s less important.
- Real-Time Updates for Breaking Queries: John Giannandrea (former head of Google Search) explained that “Freshness is about latency, not quantity.” It’s not just showing more new pages, but showing new information fast when it’s needed regmedia.co.uk. “Part of the challenge of freshness,” he testified, “is making sure that whatever gets surfaced to the top… is consistent with what people right now are interested in.” regmedia.co.uk For example, “if somebody famous dies, you kind of need to know that within seconds,” Giannandrea said regmedia.co.uk. Google built systems to handle such spikes in information demand. An internal 2021 Google document (presented in court) described a system called “Instant Glue” that feeds very fresh user-interaction data into rankings in near real-time. “One important aspect of freshness is ensuring that our ranking signals reflect the current state of the world,” the document stated. “Instant Glue is a real-time pipeline aggregating the same fractions of user-interaction signals as [the main] Glue, but only from the last 24 hours of logs, with a latency of ~10 minutes.” justice.gov In practice, this means if there’s a sudden surge of interest in a new topic (e.g. breaking news), Google’s algorithms can respond within minutes by elevating fresh results (including news articles, recent forum posts, etc.) that match the new intent. Google also uses techniques (code-named “Tetris” in one exhibit) to demote stale content for queries that deserve fresh results and to promote newsy content (e.g. Top Stories) when appropriate justice.gov
- Balancing Freshness vs. Click History: One difficulty discussed at trial is that older pages naturally accumulate more clicks over time, which could bias ranking algorithms that learn from engagement data. Nayak noted that pages with a long history tend to have higher raw click counts than brand-new pages (simply by having been around longer) regmedia.co.uk. If the system naively preferred results with the most clicks, it might favour an outdated page that users have clicked on for years, over a fresher page that hasn’t had time to garner clicks. “Clicks tend to create staleness,” as one exhibit put it regmedia.co.uk. To address this, Google “compensates” by boosting fresh content for queries where recency matters, ensuring the top results aren’t just the most popular historically, but the most relevant now. In essence, Google’s ranking algorithms include special freshness adjustments so that new, pertinent information can outrank older but formerly popular pages when appropriate regmedia.co.uk. This keeps search results timely for the user’s context.
Linking Behaviour (Link Signals and Page Reputation)
The trial also illuminated how Google uses the web’s linking behaviour – how pages link to each other – as a core ranking factor. Links serve both as votes of authority and as contextual relevance clues:
- Backlink Count & Page Reputation: Google evaluates the number and quality of links pointing to a page to gauge its prominence. Dr. Lehman explained during testimony that a ranking “signal might be how many links on the web are there that point to this web page or what is our estimate of the sort of authoritativeness of this page.”regmedia.co.uk In other words, Google’s algorithms look at the link graph of the web to estimate a page’s authority: if dozens of sites (especially reputable ones) link to Page X, that’s a strong indication that Page X is important or trustworthy on its topic. This principle underlies PageRank and other authority signals. By assessing “how many links… point to the page,” Google infers the page’s popularity and credibility within the web ecosystem regmedia.co.uk. (However, it’s not just raw counts – the quality of linking sites matters, as captured by PageRank’s “distance from a known good source” metric justice.gov.)
- Anchor Text (Link Context): Links don’t only confer authority; they also carry information. The anchor text (the clickable words of a hyperlink) tells Google what the linked page is about. As noted earlier, Pandu Nayak highlighted that anchor text provides a “valuable clue” to relevance regmedia.co.uk. For example, if dozens of sites hyperlink the text “best wireless headphones” to a particular review page, Google’s system learns that the page is likely about wireless headphones and is considered “best” by those sources, boosting its topical relevancy for that query. This context from linking behaviour helps Google align pages to queries beyond what the page’s own text says. It’s a way of leveraging the collective judgment of website creators: what phrases do others use to describe or reference your page? Those phrases become an external signal of the page’s content. Google combines this with on-page signals (as part of topicality scoring) to better understand a page’s subject matter regmedia.co.uk.
- Link Quality over Quantity: Not all links are equal. Through PageRank and related “authority” algorithms, Google gives more weight to links from reputable or established sites. One trial exhibit described PageRank as measuring a page’s proximity to trusted sites (a page linked by high-quality sites gains authority; one linked only by dubious sites gains much less) justice.gov. This shows that linking behaviour is evaluated qualitatively. A single backlink from, say, a respected news outlet or university might boost a page’s authority more than 100 backlinks from low-quality blogs. Google also works to ignore or devalue spammy linking schemes. (While specific anti-spam tactics weren’t detailed in the trial excerpts we saw, the focus on “authoritative, reputable sources” implies that links from spam networks or “content farms” are discounted – aligning with Google’s long-standing efforts to prevent link manipulation.) I go into link building more in my article on Link building for beginners.
In summary, the DOJ’s antitrust trial pulled back the curtain on Google’s ranking system.
Topicality signals (page content and context from anchors) tell Google what a page is about and how relevant it is to a query.
Authority signals (like PageRank and quality scores) gauge if the page comes from a trustworthy, reputable source.
Freshness metrics ensure the information is up-to-date when timeliness matters. And the web’s linking behaviour – both the number of links and the anchor text – feeds into both relevance and authority calculations.
All these factors, largely handcrafted and fine-tuned by Google’s engineers justice.gov, work in concert to rank the billions of pages on the web for any given search.
As Pandu Nayak summed up in court, Google uses “several hundred signals” that “work together to give [Google Search] the experience that is search today.” regmedia.co.uk
Each factor – topical relevance, authority, freshness, links, and many more – plays a part in Google’s complex, evolving ranking algorithm, with the aim of delivering the most relevant and reliable results to users.
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